We have focussed on developing datasets for gene expression using RNA-Seq, which allows us to apply a standard set of methods to a variety of model systems. Using this approach we have contributed to a number of different studies. One of the most interesting areas is in the application to the human brain, where we have a large series of brains with information on both genetic variability and gene expression. Our data has been used in many studies to determine whether a nominated genetic variant associated with a given disease or other phenotype, has a proximal biological effect on gene expression. In the current period, we have greatly expanded the number of RNA-Seq datasets that we have generated within the laboratory. We now have completed 300 postmortem samples with a matrix of high density genomic measurements. Preliminary analysis of these data are ongoing but suggest that our previous data with microarrays are robust and can be extended to look, for example, at alternative splicing in human brain.